The success of any AI-driven system relies heavily on vast amounts of training data. While AI applications in plant stress management have gained attention in recent years, there's still a significant lack of expert-annotated data, especially for tropical and subtropical crops. We're filling in this gap by releasing a public dataset with 4,749 leaf pictures of healthy, nutrient-deficient, and pest-affected tomatoes, eggplants, cucumbers, bitter gourds, snake gourds, ridge gourds, ash gourds, and bottle gourds. This dataset encompasses 57 unique classes, with high-resolution images (3024 x 3024) captured at three different sites in Bangladesh under natural field conditions. An expert panel from the Bangladesh Agricultural Research Institute (BARI) has labeled the images. This collection not only features the largest number of plant stress classes but also introduces the first multi-label classification challenge in the agricultural domain.

Early detection of plant nutritional deficiencies, followed by corrective actions, is essential for sustaining crop yield. However, identifying these early signs in plant leaves remains challenging, even with computer-aided diagnostic tools, due to their often subtle nature. To address this, we introduce a public dataset focused on three cucurbits: ash gourd, bitter gourd, and snake gourd. We chose to focus on these cucurbits due to their significant impact on global vegetable production. The dataset includes 2,700 segmented and augmented leaf images, capturing early indicators of nitrogen and potassium deficiencies alongside a healthy control group. This dataset, to the best of our knowledge, is the first to specifically target the critical early stage of nutritional stress in plants.